نشریه آب و خاک
سال سی و ششم شماره 2 (پیاپی 82، خرداد و تیر 1401)

 Simulation of quantity and quality of surface runoff in mountainous watersheds is one of the most challenging topics in modeling due to its unique features, such as unusual topography and complex hydrological processes. One of the lesser-known aspects of modeling such catchments is the uncertainty analysis of water quality predictions, especially about the vital phosphorus parameter. Phosphorus is one of the important quality variables in water, and its increase in water resources can cause eutrophication phenomena in streams and reservoirs of dams. Due to the importance of the phosphorus parameter and the fact that water quality modeling has not been employed in the Karaj catchment area so far, in this research, total phosphorus has been modeled as a water quality parameter along with the flow and sediment discharge. This study aims to identify the most sensitive parameters of the model to flow, sediment, and total phosphorus discharge and calibrate, validate and analyze the parametric uncertainty of the SWAT model in predicting these three variables in a mountainous catchment. The case study was the catchment area of ​​the Karaj River upstream of Bileqan pond, which is one of the mountainous watersheds in Iran. There are two critical water structures along the Karaj River, namely Amirkabir dam and Bilqan pond. Amirkabir dam (Karaj) is a multi-purpose project that is constructed to supply drinking water to Tehran and regulate water for irrigation and agriculture in the suburbs of Karaj. The Bileqan pond is also the essential point of supply and transfer of drinking water in Tehran. Given the importance of this region in supplying water for different uses, providing a calibrated model for quantitative and qualitative variables of water can be the basis for decisions to apply future management scenarios in this basin.

 The case study was the Karaj River catchment area upstream of Bilqan Basin, which with an average height of 2880 meters, is one of the mountainous areas located in the Alborz Mountains. This basin with an area of 1076 square kilometers in the north, includes parts of Mazandaran province. In the east and south of the catchment area includes parts of Tehran province and most of it is located in Alborz province. The average annual temperature and rainfall in this basin are 12.1 °C and 480 mm, respectively, and the average of 117 glacial days during the year is observed in this area. The long-term daily data of synoptic stations adjacent to the study area from the beginning of 1998 to the end of 2018 (21 years in total) was introduced to the model. Also, daily data of relative humidity, rainfall, minimum and maximum temperature, solar radiation hours, and wind speed as meteorological parameters measured at stations in the study area were introduced to the model. It should be noted that there was a lot of missing data in meteorological information, especially for daily temperature data. In addition to the above information, daily flow data discharged from Amirkabir dam and technical specifications of the dam were introduced to the model. In addition, orchard management information, including irrigation periods and information related to phosphate fertilizers used in regional orchards, were presented to the model. The global sensitivity analysis method was used to determine the sensitive parameters of the model. Furthermore, the SUFI2 algorithm was used in SWAT_CUP software to calibrate and analyze the parametric uncertainty of the SWAT model. This algorithm quantifies the output uncertainty by 95% prediction uncertainty boundaries.

 According to the results of sensitivity analysis, the parameters Baseflow alpha-factor (ALPHA_BF), Manning’s “n” value for overland flow (OV_N), and Precipitation Laps rate (PLAPS) were the most sensitive parameters to flow, sediment, and total phosphorus, respectively. The best Nash-Sutcliffe (NS) coefficients for runoff, sediment, and total phosphorus simulation obtained in all stations and after full calibration and validation periods were equal to 0.76, 0.56, and 0.92, respectively. Simulation of the peak points of the diagram of all three quantities was also associated with increased uncertainty and decreased model prediction accuracy, but due to the placement of more than 70% of the measured runoff and sediment values and nearly 60% of the measured total phosphorus values in the prediction uncertainty boundaries generated by SUFI2 algorithm the final value of the parameters used in the calibration process can be appropriate for simulating future scenarios in similar mountain catchments. The main weakness of the model is simulating the peak points of flow and sediment discharge. In the case of flow and sediment discharge, the liability of modeling can be generalized due to the lack of accurate prediction of the snowmelt inflow to the river in spring, which begins to increase in February and reaches the peak point in May. A considerable number of missing data in meteorological stations can effectively reflect the lack of accurate model prediction at the peak points. In this region, missing daily temperature data compared to other meteorological parameters has been significant. The dependency of the SWAT model on many experimental and quasi-experimental models such as SCS-CN and MUSLE can be another factor affecting the weakness in predicting the peak points of the sediment discharge, as well.

 According to the uncertainty analysis results, most of observed flow, sediment and total phosphorus discharge values were within the uncertainty prediction boundaries generated by the SUFI2 algorithm. The NS coefficient for all three variables has met the satisfactory modeling threshold. Therefore, it seems that the sensitive parameters identified and used in the calibration process in this study and their final values can be appropriate for modeling future scenarios for this study area and similar mountain catchments. One of the limitations of the present study was a large number of missing data in meteorological stations, especially for the temperature variable. Thus, providing required measured meteorological data to the model may emhance the simulation, especially at peak points.

The rapid growth of the world's population, followed by an increase in the need for water, has put great pressure on water resources, so it is necessary to plan for the optimal use and increase of efficiency of this vital resource. Sunflower is one of the most important oilseed crops that is mainly cultivated in Kermanshah province. Therefore, determining the appropriate sowing time of this crop for maximum production and water use efficiency is of particular importance. Because field experiments are costly and time-consuming, researchers use crop growth simulation models to determine the optimal planting time for each crop in a specific environment and climate. The use of simulation models minimizes the limitations of field experiments and allows the analysis of plant responses to environmental stresses and management scenarios. The objective of this study was to determine the optimal planting date of the Farrokh sunflower cultivar in four regions of Kermanshah province (Kermanshah, Islam Abad, Sarpol Zahab, and Kangavar) in order to maximize yield and water use efficiency using the AquaCrop model.

A field experiment was conducted at the Research Farm of Razi University, Kermanshah, Iran in order to calibrate and validate the crop parameters in the AquaCrop model. The experiment was performed in a randomized complete block design with eight irrigation treatments in three replications. The irrigation treatments were the application of 60, 80, 100, and 120% of irrigation requirement (T1, T2, T3, and T4), 20 and 40% deficit irrigation in vegetative phase (T5 and T6), and 20 and 40% deficit irrigation in reproductive phase (T7 and T8). The crop water requirement was calculated based on the daily weather data collected from an automated meteorological station at the Research Farm using the FAO Penman-Monteith equation. During the growing season, canopy cover, biomass, and soil moisture were measured weekly. The crop parameters were calibrated based on the measured data in treatments T1, T3, T6, and T7 and validated with four treatments T2, T4, T6, and T8. In the calibration and validation stages, the statistical indices including compatibility index (d) and root mean square error (RMSE) were used to evaluate the model outputs. The calibrated model was used to simulate crop growth based on daily weather data for 30 years (1988-2017) in four synoptic stations in Kermanshah province (Kermanshah, Islam Abad, Sarpol Zahab, and Kangavar) and for several different planting dates. The crop water productivity was calculated based on simulated grain yield and seasonal crop evapotranspiration. Finally, the model outputs under different planting dates were analyzed to determine the most appropriate planting time from the perspective of maximum production and maximum water use efficiency.

Statistical indicators show that the model has simulated the parameters of biomass, crop canopy, and soil moisture in the calibration stage with good accuracy. T1 and T6 treatments in biomass simulation, T7, T6, and T3 treatments in crop canopy simulation, and T3 and T7 treatments in soil moisture simulation had the highest accuracy. The accuracy of the model outputs in the validation stage for biomass and canopy cover was as accurate as in the calibration stage, while the accuracy of the simulated soil moisture in the validation stage was not high except in T4 treatment. Based on the model results, grain yield, seasonal evapotranspiration and water productivity were determined. According to the results, it can be said that in the study period (1988 -2017), grain yield has generally increased with a slight slope. The results showed that the planting date, which maximizes grain yield and water productivity, varies in the studied regions. According to the model results, planting in the second decade of May and the second decade of June will lead to the highest grain yield and water productivity in Kermanshah, respectively. Planting in the third decade of May showed the highest grain yield and crop water productivity in Islam Abad. In Sarpol Zahab, which has the highest temperature among the studied stations, planting in the last decade of March and the first decade of April has the highest grain yield and water productivity, respectively. In Kangavar, which is located in the east of Kermanshah province and has the coldest climate, by cultivating sunflower in the last decade of May and the first decade of June, respectively, the highest grain yield and water productivity can be achieved.

Due to the fact that some crop parameters of crop growth simulation models are variety specific, in this study, the crop parameters of the AquaCrop model for Farrokh sunflower cultivar were calibrated and validated. The accuracy of the calibrated model for estimating biomass and canopy cover was higher than soil moisture. The simulation results showed that the values of the studied parameters (grain yield and seasonal evapotranspiration) have changes according to the planting time in each region. The highest crop yield can be obtained in Sarpol Zahab, Islam Abad, Kermanshah, and Kangavar regions (west to east of the province) by cultivation in the last decade of March, last decade of April, the second decade of May, and last decade of May, respectively. In all study areas except Islamabad, planting date that resulted in maximum water productivity was different from the planting date that had maximum grain yield station and delayed planting had the highest water productivity.

 Barley could be grown under low-input and harsh conditions because of its wide adaptability to drought, and heat stresses. Nonetheless, the water stress leads to yield reduction when drought stress occurs during stem elongation and grain filling stages. In rainfed areas, water and heat stress occur together, specifically after anthesis, amplifying the adverse effects of water stress via disrupting water uptake of crops. In this regard, measurement of canopy temperature (Tc) by infrared thermometry is a non-destructive method that can effectively characterize the water status of plants. There is a linear relation between Tc and transpiration, which increases upon stomata closure. Since stomata is very sensitive to environmental variations and moisture reduction in the plant and it is very difficult to measure, therefore, Tc is the preferred factor to determine the crop water status. The Tc was used to calculate the practical Crop Water Stress Index (CWSI) by Idso et al. (1981) and Jackson et al. (1981). Dold et al., (2017) reported a positive significant correlation between CWSI and transpiration, daily soil water content, and plant production. Negative significant correlations between CWSI and pure photosynthesis rate, transpiration, and stomatal conductance were also reported. This study was aimed to: (i) assess the water stress effects on dryland barley genotypes using Tc, (ii) identify the upper limit for Tc affecting performance and reducing barley grain yield, (iii) determine the critical point of water stress, and (iv) apply CWSI to select the most suitable barley genotypes for both rainfed and supplemental irrigation conditions.

 To determine the crop water stress index (CWSI) and assess water status of dryland barley genotypes, an experiment was carried out in a split plot arrangement based on randomized complete block design with 15 genotypes in three replications at the Dryland Agricultural Research Institute, Maragheh (46° 45ʹ E, and 37° 26ʹ N), Iran in the 2015-2018 cropping seasons. The main plots included rainfed (as stress conditions), and supplemental irrigation (two times: 50 mm irrigation in the sowing time and 30 mm irrigation in the booting stage) as non-water stress conditions. The sub-plots included 15 barley genotypes (GaraArpa, 71411, Abidar, Ansar, ARM-ICB, ChiCm/An57//Albert, Dobrynya, Kuban-06, Makooei, Redical, Sahand, Sahand/C-25041, Sararood1, Ste/Antares//YEA762 and Valfajr). The barley genotypes were planted by Wintersteiger planter in six-row plots with 8 m long and 1.20 m wide (20 cm row spacing). The sowing rate was 380 seeds per m2 based on the thousand kernel weight (TKW) of each genotype. Seeds were treated by Penconazole fungicide. The planting dates were October 4, 2015, and October 7, 2017. In each plot, two canopy temperatures (Tc) were measured using infrared thermometer Model A-1 in six crop reproductive stages from the half of ear emerged above flag leaf ligule stage (GS55) to the soft dough stage (GS85). Measuring time was between 1:00 to 2:00 pm.

 The results indicated that the upper baseline for non–transpiring of dryland barley genotypes (Tc-Ta = 0.0008VPD + 5.89; VPD: vapor pressure deficit) was 5.9 °C (ranged from 5.5 to 6.9) which is equal to 32.4 °C green canopy and 9.0 to 11.1 mm/day evapotranspiration. Non-stressed baseline or lower baseline (Tc-Ta = -2.4662VPD + 9.15; R2 = 0.97**) showed that CWSI threshold value was 0.75 which is equal to 24.3 °C (23.7 to 26.1 °C) Tc under supplemental irrigation and 23.3 to 24.7 °C under water stress conditions. Additionally, CWSI threshold was equal to 7.3 mm/day evapotranspiration and 5.02 kPa VPD. On the other hand, results revealed that when Tc exceeded 25 °C, biological yield, thousand kernel weight (TKW) decreased significantly, followed by grain yield in different barley genotypes. The slope of the CWSI calibration equation (Tc-Ta = -2.4662VPD + 9.15) is often more negative in hot and dry areas, and tends to zero in cold and humid areas. Therefore, its negativity indicates the conditions of moisture stress for barley genotypes in the dryland phase. The CWSI threshold for barley genotypes growth stages happened at 248 (6th June) days from sowing time (4th – 7th October) which is equal to flowering stage (ZGS60). According to CWSI quantity, Ansar, ChiCm/An57//Albert, Sahand/C-25041and Ste/Antares//YEA762 were grouped in the tolerance class under stress (dryland) conditions. However, Abidar, Sahand/C-25041, GaraArpa, ChiCm/An57//Albert and Makooei were placed in the tolerance class under non-stress (supplemental irrigation) conditions.

The CWSI could estimate the intensity of heat and water stresses in the grain filling stage for barley genotypes in cold and semi-arid areas. The average of canopy temperature threshold values were 24.8 and 24.0 °C for dryland barley genotypes in supplemental irrigation and dryland conditions, respectively. In addition, these indices could be used to estimate heat and water stress tolerance levels for barley genotypes.

 In the last few decades, due to process of shifting from traditional activities and based on manual activities to industrial ones, the need for using oil and coal and its derivatives has increased. Using these materials has caused some problems for environment as hydro carbon contamination. Soil is a major contributor to the various kinds of pollution, especially hydro carbon pollution. Due to the importance of soil in the life cycles and its vitally direct and indirect influence on all the organisms and human being, elimination of this pollutant is necessary. For this reason, some different methods have been developed. In this research, capability of soil washing by sodium dodecyl sulfate ionic detergent has been measured. In order to fulfill the existing necessity and solve this problem, a wide-ranging effort has been started from the past until now, which can be referred to the issue of washing contaminated soil as one of the issues raised. At the beginning of this technology, washing with pure water was considered and after a while, it was invalidated due to inefficiency in the tested cases. With advances in this emerging technology, the discussion of stronger solvents was explored, in which detergents became more attractive due to their lower potential toxicity and environmental degradability. Actually, the effect of major parameters on removing the hydrocarbons has been investigated and in this research has been afforded to purify polluted soil with creosote by considering actual conditions in industry.

 The first sample has been taken from original soil of Razi industrial estate. It has coarse sandy loam texture with 31% clay, 11% silt and 57% of sand, pH equal 7, organic matter amount 2.3 % weight and density equal 1/8 gram per m3. Therefore, pure soil was extracted from 6 layers of soil to the depth of 0.5 m from Razi industrial area in Isfahan. Then, it was mixed by a concrete mixer specific to the block making. Afterwards, creosote was added evenly during stirring so the soil was contaminated deliberately. After storing in the laboratory for 3 weeks and homogeneity, the initial sample was chosen and its contamination was measured. This measurement was based on the amount of added oil to the certain volume of soil (about 30000 milligram in each kilogram). For avoiding error and having assurance from the amount of initial contamination, the sample was transferred to the laboratory and 25 gr of it was taken. Its hydrocarbon texture was extracted by solvent and its polar compositions were removed by passing on the silica gel absorbent. Then, a hydrocarbon was measured. The real pollutant amount of sample was 26776 milligram in each kilogram of soil. Secondary samples were chosen from basic sample, these chosen samples were washed under the different planned conditions. After finishing several complementary washing stages in different conditions, the soil samples remaining from washing were dried under different conditions. Then the amount of remained contamination in each sample was measured and recorded separately. At the next stage, the recorded results were analyzed. Stay time, temperature, pollutant concentration and washer concentration has been chosen as variable parameters.

 According to the results, washing by pure water and temperature of 30°C would not be successful but by increasing temperature, the removing efficiency increased. Increasing temperature to 90°C increased the efficiency up to 18.5%. In addition, adding detergent to the environment increased the success of this method in reducing sample pollution. Increasing efficiency up to 4 g/L of detergent increased the efficiency up to 40% directly, but there was no significant change for increasing more than this amount. At this stage, the results showed that in the presence of detergent, increasing temperature caused to increase efficiency directly. The only difference was that increasing temperature (without detergent) increased efficiency directly, but in presence of detergent, increasing efficiency was significance up to 50% and after that it increased very slightly. The last studied parameter was time. These changes included increasing efficiency due to increasing time from 10 min to 20 min. Removing pollutant efficiency has been reduced by increasing time. Under all optimum conditions, in temperature of 90°C for 20 minutes and 4 g/L surfactant, Hydrocarbon removing efficiency was 61%. The economically optimum temperature is 50oC with regard to economical cases and the slight difference resulting from increase of temperature from 50 to 90°C.

 Generally, the results revealed the suitability of ionic sodium dodecyl sulfate for cleaning soil under conditions of contamination. But 39 % of pollutant in polluted soil after washing by considering optimal conditions has been reminded. It must be mentioned that due to inefficiency of this amount of contamination reduction from contaminated soils for the discharge of these soils into the environment, this method can be introduced as a pollution reduction or a method for pretreatment of complementary methods.

Cadmium (Cd) mobility in soil is affected by various factors and its absorption from soil by tobacco is higher than other crops. Application of phosphate fertilizers in agricultural lands is an essential step to increase the yield of tobacco plants. Since most phosphate fertilizers contain small amounts of Cd, the uptake of Cd by tobacco plant in its cultivated areas due to the application of triple superphosphate fertilizer (TSP) is not unexpected. In many tobacco growing areas, the water or soil used is between low and medium salinity in terms of salinity, which can also influence the solubility of cadmium and, consequently, its uptake by tobacco plant. Cadmium can be absorbed through food, drink and respiration. This metal not only is absorbed by the digestive organs, but also is absorbed by the respiratory organs through airborne particles and cigarette smoke. Tobacco is resistant to high concentrations of Cd in soil and can absorb it from Cd-contaminated soil. The aim of this study is to investigate the effect of P fertilizer and salinity on Cd mobility in soil and tobacco plant.

This experiment was conducted with the aim of investigating the interaction of three factors of irrigation salinity (0, 20 and 40 mM NaCl), triple tuper phosphate fertilizer (TSP) (0 and 1.5 g kg-1 soil) and soil Cd contamination level (0 and 12 mg kg-1 soil) in a completely randomized design with four replications on shoot Cd concentration, smoke Cd concentration, extraction percentage of DTPA, tobacco ash Cd concentration, Cd mobility factor and Cd fractions in soil. To homogenize the samples, they were thoroughly mixed together and the resulting composite samples were passed through a 2 mm sieve to incubate the samples and then implant. Cadmium contamination levels (0 and 12 mg kg-1) were prepared from Cd(NO3)2.4H2O source. Prior to planting, the relevant levels of contamination were added by spraying on the entire soil surface and mixed thoroughly. Soil samples were transferred to plastic storage containers and incubated for four months in a controlled greenhouse within a temperature range of 25-30 °C and 70% water holding capacity of the soil measured by the weighing method. Cultivation was carried out under controlled conditions in a greenhouse environment located in Bardaskan city. Two 60-day-old tobacco seedlings (Nicotiana tabacum L.) of Cocker 347 cultivar, which were previously seeded in non-contaminated cadmium soil and grown with non-saline water, were transferred to each pot and planted. The cultivar used in this experiment was a greenhouse tobacco cultivar used in the cigarette industry. Immediately after transferring the seedlings to pots, irrigation was performed with saline-free water (distilled water), salinity of 20 or 40 mM NaCl salt for 75 days according to the required treatment. Up to the fourth week, the amount of 400 ml per pot in each irrigation cycle, and after that until the end of the experiment, the amount of 800 ml per pot in each irrigation cycle was applied.

The results showed that Cd mobility factor in Cd-contaminated soil increased on average by 25.6%, 32.4% and 36.2% compared to non-contaminated soil at 0, 20 and 40 mM salinity, respectively. Application of phosphate fertilizer significantly reduced the mobility factor of cadmium in non-cadmium-contaminated soils. In Cd-contaminated soil, the extraction percentage of DTPA increased 26.5% and 56.4% with increasing irrigation salinity levels from 0-20 and 0-40, respectively. In non-Cd contaminated soil, TSP application reduced extraction percentage of DTPA 20.2%, 28.4% and 24.6% in 0, 20 and 40 irrigation salinity levels, respectively in compared to non-TSP application. With increasing the levels of soil Cd contamination, the percentage Cd concentration in oxide fraction of soil decreased and the percentage of Cd concentration in carbonate, organic and residual fractions increased. Application of TSP increased the concentration of residual Cd fraction in the soil.

With increasing the level of Cd contamination in soil, the percentage of Cd in carbonate and organic fractions increased compared to non-Cd contaminated soil. The results showed that TSP application in Cd contaminated soil in salinitylevels of 0, 20, and 40 mM increased Cd concentration of tobacco ash by 1.47%, 15.89% and 29.80% and increased Cd concentration of tobacco smoke by 23.20%, 23.30% and 18%, respectively. Salinity factor and phosphate fertilizer showed the reverse effect on soluble + exchangeable cadmium and DTPA available Cd in soil, so with increasing salinity, these concentrations increased and with increasing triple superphosphate fertilizer decreased.

Saffron, which its cultivation is compatible with the arid and semi-arid climate of Iran, is one of the most valuable agricultural products in the world. Therefore, the cultivation of this crop in different parts of the country has been enormously developed in recent years. More than 95% of the world production of this precious product is allocated to Iran, which is mainly located in the two provinces of Khorasan Razavi and Southern Khorasan. The objective of this study was to determine the priority of lands for saffron cultivation by using TOPSIS method. Furthermore, in this study, TOPSIS, which is the second most widely used approach among multi-criteria decision making methods, was compared with the conventional parametric one to assess the land suitability for saffron production.

To achieve the objective of this study, 135 saffron farms in Khorasan Razavi, Southern Khorasan, Fars, Markazi and Kerman provinces were selected. In each farm, one pedon was dug and studied in detail. Soil samples were collected from different horizons of the pedons and taken to the laboratory for the designated physicochemical analyses. The average quantity of saffron yield in the last three years was recorded for each study point. The selected areas did not have climatic restrictions for saffron cultivation. For this purpose, in addition to local experience, the climate suitability index was calculated using the saffron climatic requirement table by its phenological period in each region. The effective soil criteria conditioned on the saffron yield were obtained using statistical analyses. By constructing a decision matrix and normalizing it, weighting the criteria by ranking order method and constructing a weighted matrix, determining the positive and negative ideal and then calculating the relative proximity of each alternative to the positive ideal, the preference of each alternative by TOPSIS method for saffron cultivation was determined. Then, the prioritization of alternatives was compared with the actual yield of saffron. Soil suitability index was also calculated using the table of soil and landscape requirements for saffron, and then compared with actual yield. Finally, the two schemes were validated and compared with each other.

The climate suitability index for saffron cultivation in the five studied areas indicated that the climate conditions in all areas were relatively similar. Consequently, soil properties can be considered as the only factors affecting the priority of lands for saffron cultivation in the studied areas. The results further revealed that three variables of lime content, salinity and exchangeable sodium percentage of soils under saffron cultivation in the country were higher than the critical level for saffron production. Therefore, these three variables are considered as the most important soil properties affecting the saffron yield. The order of weights assigned to the variables included salinity, exchangeable sodium percentage, lime, gravel, gypsum, organic carbon and soil reaction. Comparison of the order of priority of 135 options by TOPSIS with the actual yield of saffron showed an acceptable accuracy (R2 = 0.92) for this method. The soil index calculated by the parametric square root method for 135 soil profiles was also compared with the actual yield. The coefficient of determination obtained in this case was about 0.9, showing that TOPSIS was able to determine the suitability of lands for saffron cultivation better than the parametric method. Due to the ability of TOPSIS to evaluate a large number of evaluation criteria, this method is superior to the parametric method, which can consider a maximum of eight criteria in estimating the index.

The outcome of this study showed a high accuracy of TOPSIS method in determining land suitability for development of saffron cultivation. This method is well able to use a large number of criteria that have negative or positive effects on the priority of alternatives. Furthermore, depending on the conditions of the decision making problem, one of the methods of weighting the criteria can be employed and combined with the TOPSIS method. The high accuracy of this method can be attributed to the use of mathematical relationships and matrices, data standardization by Euclidean soft method, and the nature of comparing both distances from the positive and negative ideals.

 Phosphorus is an essential soil nutrient that plays key roles in plant growth and development. Limited availability of P is the main constraint for crop production in many soils. Long-term phosphate fertilizers application in agricultural areas to increase the physiological efficiency of crops can lead to a significant P accumulation. The process of P fixation or sorption includes precipitation and adsorption onto mineral and organic surfaces. Various factors such as clay content, organic matter, exchangeable Al, Fe, Ca content and pH soil affect P sorption capacity. In order to achieve the proper management of P fertilization, it is necessary to understand the mechanism of the sorption process and the contributing factors, as well as how to influence these factors. Qazvin plain is one of the most important agricultural plains in Iran, playing a pivotal role in maintaining national food security. Cultivating crops such as wheat, barley, alfalfa and corn in different areas of this plain is widespread. Therefore, high amounts of phosphate fertilizers are applied in this plain every year. In this study, the kinetic and equilibrium adsorption of P in a heavy textured agricultural soil sample in Qazvin plain were investigated under the influence of some different environmental parameters.

In order to conduct the kinetic adsorption experiment, one gram soil samples were placed in the shaker in contact with 25 ml of 0.01 M CaCl2 solution containing 20 mg P l-1. Time intervals were 0.17, 0.5, 1, 2, 4, 8, 16, 24, 48 and 72 hours. The effects of temperature (12, 25, 38 °C), salinity (0, 8.96, 17.02, 32.09, 46.25 dS m-1), pH (2.5, 3.5, 5.36, 7.5, 9.5, 11.5) and the type of background solution (distilled water and 0.01 M CaCl2 solution) were also investigated on P equilibrium adsorption. In the equilibrium batch experiments, the soil samples were placed in contact with the background solutions containing 0, 15, 20, 30, 50, 80 and 100 mg P l-1 (ratio 1:25) for 24 hours. The concentration of P in the samples was determined by a spectrophotometer after passing through the filter. The amount of P adsorption to each soil sample was then calculated based on the concentrations. The experiments were carried out in the factorial and completely randomized designs with three replications for each treatment. Using CurveExpert 1.4 software, the Langmuir and Freundlich isotherms, as well as the pseudo-first-order, pseudo-second-order, the Elovich and Intra-particle diffusion models were fitted to the obtained laboratory data. Statistical analysis of experimental data was done based on the Tukey test at 5% level using Minitab software. The thermodynamics of P adsorption was also determined by examining parameters of the Gibbs free energy, enthalpy and entropy changes.

 According to the results, the highest amount of adsorption occurred in the first 8 hours of soil contact with P solution, and approximate time of achieving the equilibrium conditions was 24 to 48 hours. The process of P adsorption onto soil particles consisted of two fast and slow stages until the equilibrium was reached. The kinetic adsorption properties of the studied soil was best described by the Elovich equation (r2=0.964). The Freundlich model showed better fit than the Langmuir equation to the equilibrium data. The effects of all four parameters of temperature, salinity, pH and background electrolyte solution on the P equilibrium adsorption were significant. By changing the temperature from 25 to 38 °C, qm (Langmuir coefficient) was 2.1 times. It was also 7.5 times under the conditions of using CaCl2 solution instead of distilled water. Increasing pH caused an increase in adsorption rate and the highest amount of adsorption changes occurred in the pH varying between 5.36 and 7.5. However, the highest and lowest P adsorption percentage with the values of 45 and 37% were related to zero and 46.25 dS m-1 salinity, respectively. The results also indicated that the sorption process was endothermic and spontaneous.

 Adjusting and controlling the studied parameters in the soil during the application of phosphate fertilizers can optimize P use efficiency and increase crop yield in the studied area. Based on the results of the present study, it is recommended to add sulfur, ammonium sulfate, ammonium nitrate fertilizers and organic compounds to the studied calcareous soil with high pH and low salinity. Application of this method can reduce soil pH, which leads to a decreased P sorption onto the soil particles and an enhanced P availability for plants. Adjusting the P fertilization time with the crop growth and uptake is also recommended due to the high adsorption of P onto the soil particles in a short period of time.

Global observations have confirmed that in recent decades, forests have been converted into agricultural land at a swift pace; this is a major global concern. Forests around the world have also experienced severe disturbances due to other anthropogenic activities. The conversion of forests to cropland often results in soil degradation. Slope gradient and land use change are known to influence soil quality; therefore, the assessment of soil quality is important in determining sustainable land-use and soil-management practices. Magnetic susceptibility (χlf) measurements are widely used to study soil-forming processes. Many efforts have been made to correlate soil magnetic susceptibility with different soil properties, such as topography, parent material, Fe oxide forms, etc. The Yasouj area of Kohgilouye Province is one of the most densely forested areas in Zagros mountainous region. Parts of the area have been cultivated to feed the growing population, which has led to forest degradation. The objectives of this study were to assess some soil properties focusing on soil χlf and Fe- oxides forms in different land uses and slope positions.

 Forty soil samples were taken from dense forest, sparse forest, eroded lands and dryland farming from different slops (0-15 and 15-30 percent) in Mokhtar Plain, west of Yasouj city. Soil samples were taken from the depth of 0–15 cm in a completely randomized design with five replications. Soil moisture and temperature regimes in the study area are xeric and thermic, respectively. Particle size distribution was determined by the hydrometer method and soil organic matter, CaCO3 equivalent and bulk density were determined using standard procedures. Fe (Feo) were extracted by acid ammonium oxalate, using a single 4-h extraction at pH 3 in the dark. Total free iron (Fed) was extracted with the CBD method. The total Fe contents (Fet) in the soil samples were determined after extraction with 5 mol L-1 HNO3. Magnetic susceptibility of the soils was measured at low (0.46 kHz; χlf) and high (4.6 kHz; χhf) frequencies, respectively; using a Bartington MS2 dual-frequency sensor, with approximately 10 g of air-dry soil in polyethylene vials. The percentage of frequency-dependent magnetic susceptibility (χfd%) was calculated to study the size of magnetic crystals in soils and the abundance of pedogenic ferrimagnetic in SP-SSD (~0.03 μm) boundary.

The results of this study showed that the land use and slope positions were among the important factors affecting the change of soil properties in this area. Land use change along with the reduction of organic matter reduced the stability of aggregates and increased land erosion. This process caused the loss of clay particles and magnetic minerals and affected many soil properties. Organic matter as an important indicator of soil quality, showed a decrease of about 3 times as a result of land use change from dense forest to eroded lands following by an increase in bulk density and a decrease in soil permeability and other soil quality indicators. Long-term afforestation and agricultural activities on sloping lands changed the soil texture from a class of silty loam in the forest to a lighter class of silty loam in agricultural use. Soil magnetic susceptibility, which is a function of soil magnetic particles was greatly affected by land use change and to a lesser extent by slope position. Due to the fact that magnetic susceptibility is influenced by factors such as soil texture, drainage class, erosion conditions, magnetic mineral contents, soil evolution conditions, land use changes from forest to other uses had significant effects (about 2 times) on χlf. Significant decrease in the amount of calcium carbonate in low slope positions was another reason for the increase in magnetic susceptibility in these positions. According to the measured values ​​of χfd (ranged from 1.9 to 7.2%), the magnetic particles of the soils had low to moderate amounts of superparamagnetic (SP) particles, which indicates the combined effect of pedogenic superparamagnetic ultrafine particles and  lithogenic (inherited) magnetic particles in χlf of the soils. The effect of slope on Fe forms (Feo, Fed and Fet) has been significant (p < 0.01) in almost all land uses. Due to the relatively high correlation of χlf with some soil properties such as Fe forms, soil clay, the amount of diamagnetic compounds including calcium carbonate in the studied soils, it is possible to estimate the value of these soil properties using χlf, which is a quick and cost-effective approach. Overall, it seems that magnetic susceptibility could be applied successfully to estimate some soil properties in hilly regions of Zagros Mountains of southwestern Iran, especially for monitoring the consequences of land use changes. It should also be noted that any change in the use of the area should be defined in accordance with the potential of the land in the long term to prevent a reduction in soil quality.

 Estimating soil properties on large scales using experimental methods requires specialized equipments and can be extremely time-consuming and expensive, especially when dealing with a high spatial sampling density. Soil Visible and Near-InfraRed (V-NIR) reflectance spectroscopy has proven to be a fast, cost-effective, non-destructive, environmental-friendly, repeatable, and reproducible analytical technique. V-NIR reflectance spectroscopy has been used for more than 30 years to predict an extensive variety of soil properties like organic and inorganic carbon, nitrogen, organic carbon, moisture, texture and salinity. The objectives of this study were to estimate soil properties (carbonate calcium equivalent (CCE), electrical conductivity (EC), pH, and organic carbon (OC)) using visible near-infrared and short-wave Infrared (SWIR) reflectance spectroscopy (350-2500 nm). In this study, the best predictions of all the soil properties, model and pre-processing technique were also determined. The Partial Least Squares Regression (PLSR), Artificial Neural Network, Support Vector Machine Regression and Principal Component Regression (PCR) models were also compared to estimate soil properties.

 A total number of 200 surface soil samples (0-10 cm) were collected from the Semirom region (51º 17' - 52º 3' E; 30º 42' - 31º 51' N), Isfahan, Iran. The samples were air dried and passed through a 2 mm sieve, and using standard procedures soil properties were determined in the laboratory. Accordingly, soil pH and the EC contents of soil samples were determined in saturated pastes and extracts, respectively. The CCE content of the soils were measured using back titration, and the OC contents of the samples were measured using Walkley-Black method. The Reflectance spectra of all samples were measured using an ASD field spectrometer. The selection of the best model was done according to the value of the Ratio of Performance to Deviation (RPD), the coefficient of determination (R2), and the Root Mean Square Eerror (RMSE).

 Once the models were constructed using PLSR, ANN, SVMR and PCR approaches, descriptive analysis was carried out for each property, for the data measured in the laboratory. The parameters calculated for the properties were mean, coefficient of variation (CV), minimum and maximum, standard deviation and range. Coefficient of variation for the organic carbon, CCE, pH, and EC values were 21.7, 12.4, 1.34, and 28.74, respectively. Wilding (1985) proposed low, medium, and high variability for the CV values less than 15%, 15-35%, and greater than 35%, respectively. Accordingly, the organic carbon and EC of soils could be classified in the group with moderate variability. However, the calcium carbonate equivalent and pH are in the group with low variability. Since spectral data preprocessing has an effective role on improving the calibration, in order to perform spectral preprocessing, two first nodes at the first (350-400 nm) and the end (2450-2500 nm) of each spectrum were removed. In addition, two interruptions were eliminated, due to the change in the detector in the range of 900 to 1700 nm. Different preprocessing methods i.e., Standard Normal Variable (SNV) and First (FD) and Second Derivatives (SD) and Savitzky-Golay preprocessing techniques were performed on spectral data. Then, using PLSR, the cross‐validation method was used to evaluate soil properties calibration and validation. According to Stenberg (2002), for agricultural applications, The values of RPD greater than 2 indicate that the models provide precise predictions, the values of RPD between 1.5 and 2 are considered to be reasonably representative, and the values of RPD less than 1.5 indicate poor predictive performance. The results indicated the desirable capability of the PLSR method in estimating the EC (RPD > 2, R2 = 0.94), CCE (RPD > 2, R2 = 0.88), and OC (RPD > 2, R2 = 0.89). The best results of the pH (RPD > 2, R2 = 0.79) were estimated by the SVMR method. In this study the best methods of preprocessing techniques were First (FD) and Second Derivatives (SD) and Savitzky-Golay filter.

 In general, based on the results of this study, VNIR spectroscopy was successful in estimating soil properties and showed its potential for substituting laboratory analyses. Moreover, spectroscopy could be considered as a simple, fast, and low-cost method in predicting soil properties. The PLSR model with First and Second derivatives and Savitzky-Golay pre-processing techniques seems to be more robust algorithm for estimating EC, OC, and CCE. The best results of the pH were estimated by the SVMR method with First and Second derivatives and Savitzky-Golay pre-processing techniques.

 Drought analysis in agriculture can not only be achieved by measuring precipitation changes but also by using other parameters such as soil moisture. Due to the fact that soil moisture affects plant growth and yield, it is often considered for monitoring agricultural drought. Remote sensing data are often provided from three sources: microwave, visible and thermal. Most satellite soil moisture-based algorithms rely on passive microwave images, active microwaves, or a combination of data from several different sensors. Among the various remote sensing methods, the microwave electromagnetic spectrum has fewer physical limitations than other spectrum in measuring soil moisture. However, microwave soil moisture data often have very large pixel dimensions (more than 10 km), making it difficult to use them on a small scale.

 In this study, in order to calculate the agricultural drought index at the field-scale, AMSR2 Retrieval data were calibrated first using field moisture measurement data in the Neishabour plain during 2017 to 2019. During the research period, 560 soil samples (20 samples in 28 shifts) were collected and soil moisture was measured in the laboratory of the Department of Water Science and Engineering, Ferdowsi University of Mashhad. LPRM_AMSR2_ SOILM3_001 is one of the third level products of the AMSR2 sensor, which is produced on a daily basis with a spatial resolution of 25 × 25 km2. Land surface parameters including surface temperature, surface soil moisture and plant water availability were obtained by passive microwave data using the Land parameter Retrieval Method (LPRM). Then, by using Modis sensor images (NDVI and LST), linear downscaling equations were extracted. The dimensions of the AMSR2 images were reduced from 25 kilometers to 1000 meters using these equations. In next step, SMADI Agricultural Drought Index, which is a combination of vegetation characteristics, soil moisture and land surface temperature, was used to monitor agricultural drought at the field-scale. Statistical indicators such as coefficient of determination (R^2), mean absolute error (MAE) and root mean square error (RMSE) were also used to evaluate the statistical performance.

By visual analysis of the role of vegetation and land unevenness, it was found that these two factors affect the regression relationships extracted for calibration of remote sensing data. The RMSE and MAE values for the regression equations used in the calibration process were calculated in the range of 1.6 to 4%, which can be considered acceptable in comparison with the mean values of the soil moisture data (15 to 20). The results showed that changes in SMADI index in three land use zones including rainfed cultivation (R1), medium rangeland (R2) and poor rangeland (R3) have experienced a similar trend to precipitation changes, illustrating that precipitation is one of the most effective factors in major changes in SMADI agricultural drought index fluctuations. It was also observed that SMADI index changes with a delay of 1 to 8 days compared to the precipitation changes in all three zones. In all three zones, the SMADI index followed a similar trend to in-situ soil moisture changes. At mot 80% of the changes in SMADI-R1 index can be explained by in-situ SM-R1, and the rest of the changes were related to other environmental factors or measurement error. This decreases to 68% in the R3 zone. It should be noted that soil moisture monitoring can more accurately reflect the impact of environmental factors on the changes in agricultural drought index such as SMADI than other variables; because the rainfall recorded at the meteorological station does not necessarily occur uniformly throughout the study area. On the other hand, any amount of precipitation will not necessarily lead to an effective change in soil moisture storage. This also renders assessment of the performance of agricultural drought indicators difficult.

 Examination of statistical indices of coefficient of determination (R2), mean absolute error value (MAE) and root mean square error (RMSE) showed that the algorithm used in downscaling as well as estimating SMADI agricultural drought index is well able to reflect the interactions between precipitation, soil moisture, vegetation and changes in canopy temperature profile. This feature justifies and strengthens its application in agrometeorological analysis.